Overview

Dataset statistics

Number of variables50
Number of observations131
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.3 KiB
Average record size in memory401.0 B

Variable types

Categorical27
Numeric23

Warnings

Total Bilirubin(mg/dL) is highly correlated with Direct Bilirubin (mg/dL)High correlation
Direct Bilirubin (mg/dL) is highly correlated with Total Bilirubin(mg/dL)High correlation
Grams of Alcohol per day has 28 (21.4%) zeros Zeros
Packs of cigarets per year: has 46 (35.1%) zeros Zeros

Reproduction

Analysis started2021-05-15 08:57:38.475650
Analysis finished2021-05-15 08:58:52.739487
Duration1 minute and 14.26 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Gender
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
115 
female
16 

Length

Max length6
Median length4
Mean length4.244274809
Min length4

Characters and Unicode

Total characters556
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male115
87.8%
female16
 
12.2%
2021-05-15T14:28:52.917906image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:53.017638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male115
87.8%
female16
 
12.2%

Most occurring characters

ValueCountFrequency (%)
e147
26.4%
m131
23.6%
a131
23.6%
l131
23.6%
f16
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter556
100.0%

Most frequent character per category

ValueCountFrequency (%)
e147
26.4%
m131
23.6%
a131
23.6%
l131
23.6%
f16
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin556
100.0%

Most frequent character per script

ValueCountFrequency (%)
e147
26.4%
m131
23.6%
a131
23.6%
l131
23.6%
f16
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII556
100.0%

Most frequent character per block

ValueCountFrequency (%)
e147
26.4%
m131
23.6%
a131
23.6%
l131
23.6%
f16
 
2.9%

Symptoms
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
89 
female
42 

Length

Max length6
Median length4
Mean length4.641221374
Min length4

Characters and Unicode

Total characters608
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male89
67.9%
female42
32.1%
2021-05-15T14:28:53.198654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:53.306370image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male89
67.9%
female42
32.1%

Most occurring characters

ValueCountFrequency (%)
e173
28.5%
m131
21.5%
a131
21.5%
l131
21.5%
f42
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter608
100.0%

Most frequent character per category

ValueCountFrequency (%)
e173
28.5%
m131
21.5%
a131
21.5%
l131
21.5%
f42
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin608
100.0%

Most frequent character per script

ValueCountFrequency (%)
e173
28.5%
m131
21.5%
a131
21.5%
l131
21.5%
f42
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII608
100.0%

Most frequent character per block

ValueCountFrequency (%)
e173
28.5%
m131
21.5%
a131
21.5%
l131
21.5%
f42
 
6.9%

Alcohol
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
103 
female
28 

Length

Max length6
Median length4
Mean length4.427480916
Min length4

Characters and Unicode

Total characters580
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male103
78.6%
female28
 
21.4%
2021-05-15T14:28:53.536998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:53.610840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male103
78.6%
female28
 
21.4%

Most occurring characters

ValueCountFrequency (%)
e159
27.4%
m131
22.6%
a131
22.6%
l131
22.6%
f28
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter580
100.0%

Most frequent character per category

ValueCountFrequency (%)
e159
27.4%
m131
22.6%
a131
22.6%
l131
22.6%
f28
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin580
100.0%

Most frequent character per script

ValueCountFrequency (%)
e159
27.4%
m131
22.6%
a131
22.6%
l131
22.6%
f28
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII580
100.0%

Most frequent character per block

ValueCountFrequency (%)
e159
27.4%
m131
22.6%
a131
22.6%
l131
22.6%
f28
 
4.8%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
117 
male
14 

Length

Max length6
Median length6
Mean length5.786259542
Min length4

Characters and Unicode

Total characters758
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale
ValueCountFrequency (%)
female117
89.3%
male14
 
10.7%
2021-05-15T14:28:53.809315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:53.883160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female117
89.3%
male14
 
10.7%

Most occurring characters

ValueCountFrequency (%)
e248
32.7%
m131
17.3%
a131
17.3%
l131
17.3%
f117
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter758
100.0%

Most frequent character per category

ValueCountFrequency (%)
e248
32.7%
m131
17.3%
a131
17.3%
l131
17.3%
f117
15.4%

Most occurring scripts

ValueCountFrequency (%)
Latin758
100.0%

Most frequent character per script

ValueCountFrequency (%)
e248
32.7%
m131
17.3%
a131
17.3%
l131
17.3%
f117
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII758
100.0%

Most frequent character per block

ValueCountFrequency (%)
e248
32.7%
m131
17.3%
a131
17.3%
l131
17.3%
f117
15.4%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
130 
male
 
1

Length

Max length6
Median length6
Mean length5.984732824
Min length4

Characters and Unicode

Total characters784
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female130
99.2%
male1
 
0.8%
2021-05-15T14:28:54.081398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:54.172155image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female130
99.2%
male1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter784
100.0%

Most frequent character per category

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin784
100.0%

Most frequent character per script

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII784
100.0%

Most frequent character per block

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
101 
male
30 

Length

Max length6
Median length6
Mean length5.541984733
Min length4

Characters and Unicode

Total characters726
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale
ValueCountFrequency (%)
female101
77.1%
male30
 
22.9%
2021-05-15T14:28:54.394559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:54.486353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female101
77.1%
male30
 
22.9%

Most occurring characters

ValueCountFrequency (%)
e232
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f101
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter726
100.0%

Most frequent character per category

ValueCountFrequency (%)
e232
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f101
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin726
100.0%

Most frequent character per script

ValueCountFrequency (%)
e232
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f101
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII726
100.0%

Most frequent character per block

ValueCountFrequency (%)
e232
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f101
13.9%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
106 
male
25 

Length

Max length6
Median length6
Mean length5.618320611
Min length4

Characters and Unicode

Total characters736
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female106
80.9%
male25
 
19.1%
2021-05-15T14:28:54.685782image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:54.769558image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female106
80.9%
male25
 
19.1%

Most occurring characters

ValueCountFrequency (%)
e237
32.2%
m131
17.8%
a131
17.8%
l131
17.8%
f106
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter736
100.0%

Most frequent character per category

ValueCountFrequency (%)
e237
32.2%
m131
17.8%
a131
17.8%
l131
17.8%
f106
14.4%

Most occurring scripts

ValueCountFrequency (%)
Latin736
100.0%

Most frequent character per script

ValueCountFrequency (%)
e237
32.2%
m131
17.8%
a131
17.8%
l131
17.8%
f106
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII736
100.0%

Most frequent character per block

ValueCountFrequency (%)
e237
32.2%
m131
17.8%
a131
17.8%
l131
17.8%
f106
14.4%

Cirrhosis
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
120 
female
 
11

Length

Max length6
Median length4
Mean length4.167938931
Min length4

Characters and Unicode

Total characters546
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male120
91.6%
female11
 
8.4%
2021-05-15T14:28:54.962993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:55.046764image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male120
91.6%
female11
 
8.4%

Most occurring characters

ValueCountFrequency (%)
e142
26.0%
m131
24.0%
a131
24.0%
l131
24.0%
f11
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter546
100.0%

Most frequent character per category

ValueCountFrequency (%)
e142
26.0%
m131
24.0%
a131
24.0%
l131
24.0%
f11
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin546
100.0%

Most frequent character per script

ValueCountFrequency (%)
e142
26.0%
m131
24.0%
a131
24.0%
l131
24.0%
f11
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII546
100.0%

Most frequent character per block

ValueCountFrequency (%)
e142
26.0%
m131
24.0%
a131
24.0%
l131
24.0%
f11
 
2.0%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
123 
male
 
8

Length

Max length6
Median length6
Mean length5.877862595
Min length4

Characters and Unicode

Total characters770
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female123
93.9%
male8
 
6.1%
2021-05-15T14:28:55.242328image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:55.315138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female123
93.9%
male8
 
6.1%

Most occurring characters

ValueCountFrequency (%)
e254
33.0%
m131
17.0%
a131
17.0%
l131
17.0%
f123
16.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter770
100.0%

Most frequent character per category

ValueCountFrequency (%)
e254
33.0%
m131
17.0%
a131
17.0%
l131
17.0%
f123
16.0%

Most occurring scripts

ValueCountFrequency (%)
Latin770
100.0%

Most frequent character per script

ValueCountFrequency (%)
e254
33.0%
m131
17.0%
a131
17.0%
l131
17.0%
f123
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII770
100.0%

Most frequent character per block

ValueCountFrequency (%)
e254
33.0%
m131
17.0%
a131
17.0%
l131
17.0%
f123
16.0%

Smoking
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
85 
female
46 

Length

Max length6
Median length4
Mean length4.702290076
Min length4

Characters and Unicode

Total characters616
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male85
64.9%
female46
35.1%
2021-05-15T14:28:55.531661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:55.614993image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male85
64.9%
female46
35.1%

Most occurring characters

ValueCountFrequency (%)
e177
28.7%
m131
21.3%
a131
21.3%
l131
21.3%
f46
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter616
100.0%

Most frequent character per category

ValueCountFrequency (%)
e177
28.7%
m131
21.3%
a131
21.3%
l131
21.3%
f46
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Latin616
100.0%

Most frequent character per script

ValueCountFrequency (%)
e177
28.7%
m131
21.3%
a131
21.3%
l131
21.3%
f46
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII616
100.0%

Most frequent character per block

ValueCountFrequency (%)
e177
28.7%
m131
21.3%
a131
21.3%
l131
21.3%
f46
 
7.5%

Diabetes
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
85 
male
46 

Length

Max length6
Median length6
Mean length5.297709924
Min length4

Characters and Unicode

Total characters694
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowfemale
ValueCountFrequency (%)
female85
64.9%
male46
35.1%
2021-05-15T14:28:55.817410image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:55.895248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female85
64.9%
male46
35.1%

Most occurring characters

ValueCountFrequency (%)
e216
31.1%
m131
18.9%
a131
18.9%
l131
18.9%
f85
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter694
100.0%

Most frequent character per category

ValueCountFrequency (%)
e216
31.1%
m131
18.9%
a131
18.9%
l131
18.9%
f85
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin694
100.0%

Most frequent character per script

ValueCountFrequency (%)
e216
31.1%
m131
18.9%
a131
18.9%
l131
18.9%
f85
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII694
100.0%

Most frequent character per block

ValueCountFrequency (%)
e216
31.1%
m131
18.9%
a131
18.9%
l131
18.9%
f85
 
12.2%

Obesity
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
118 
male
13 

Length

Max length6
Median length6
Mean length5.801526718
Min length4

Characters and Unicode

Total characters760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female118
90.1%
male13
 
9.9%
2021-05-15T14:28:56.088773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:56.168589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female118
90.1%
male13
 
9.9%

Most occurring characters

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter760
100.0%

Most frequent character per category

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin760
100.0%

Most frequent character per script

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII760
100.0%

Most frequent character per block

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Hemochromatosis
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
126 
male
 
5

Length

Max length6
Median length6
Mean length5.923664122
Min length4

Characters and Unicode

Total characters776
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female126
96.2%
male5
 
3.8%
2021-05-15T14:28:56.365290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:56.444086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female126
96.2%
male5
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e257
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f126
16.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter776
100.0%

Most frequent character per category

ValueCountFrequency (%)
e257
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f126
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin776
100.0%

Most frequent character per script

ValueCountFrequency (%)
e257
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f126
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII776
100.0%

Most frequent character per block

ValueCountFrequency (%)
e257
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f126
16.2%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
80 
male
51 

Length

Max length6
Median length6
Mean length5.221374046
Min length4

Characters and Unicode

Total characters684
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
female80
61.1%
male51
38.9%
2021-05-15T14:28:56.664597image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:56.773309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female80
61.1%
male51
38.9%

Most occurring characters

ValueCountFrequency (%)
e211
30.8%
m131
19.2%
a131
19.2%
l131
19.2%
f80
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter684
100.0%

Most frequent character per category

ValueCountFrequency (%)
e211
30.8%
m131
19.2%
a131
19.2%
l131
19.2%
f80
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Latin684
100.0%

Most frequent character per script

ValueCountFrequency (%)
e211
30.8%
m131
19.2%
a131
19.2%
l131
19.2%
f80
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII684
100.0%

Most frequent character per block

ValueCountFrequency (%)
e211
30.8%
m131
19.2%
a131
19.2%
l131
19.2%
f80
 
11.7%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
118 
male
13 

Length

Max length6
Median length6
Mean length5.801526718
Min length4

Characters and Unicode

Total characters760
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowmale
ValueCountFrequency (%)
female118
90.1%
male13
 
9.9%
2021-05-15T14:28:56.951838image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:57.041593image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female118
90.1%
male13
 
9.9%

Most occurring characters

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter760
100.0%

Most frequent character per category

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin760
100.0%

Most frequent character per script

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII760
100.0%

Most frequent character per block

ValueCountFrequency (%)
e249
32.8%
m131
17.2%
a131
17.2%
l131
17.2%
f118
15.5%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
130 
male
 
1

Length

Max length6
Median length6
Mean length5.984732824
Min length4

Characters and Unicode

Total characters784
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female130
99.2%
male1
 
0.8%
2021-05-15T14:28:57.247096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:57.320894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female130
99.2%
male1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter784
100.0%

Most frequent character per category

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin784
100.0%

Most frequent character per script

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII784
100.0%

Most frequent character per block

ValueCountFrequency (%)
e261
33.3%
m131
16.7%
a131
16.7%
l131
16.7%
f130
16.6%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
125 
male
 
6

Length

Max length6
Median length6
Mean length5.908396947
Min length4

Characters and Unicode

Total characters774
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female125
95.4%
male6
 
4.6%
2021-05-15T14:28:57.507958image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:57.587746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female125
95.4%
male6
 
4.6%

Most occurring characters

ValueCountFrequency (%)
e256
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f125
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter774
100.0%

Most frequent character per category

ValueCountFrequency (%)
e256
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f125
16.1%

Most occurring scripts

ValueCountFrequency (%)
Latin774
100.0%

Most frequent character per script

ValueCountFrequency (%)
e256
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f125
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII774
100.0%

Most frequent character per block

ValueCountFrequency (%)
e256
33.1%
m131
16.9%
a131
16.9%
l131
16.9%
f125
16.1%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
77 
male
54 

Length

Max length6
Median length6
Mean length5.175572519
Min length4

Characters and Unicode

Total characters678
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female77
58.8%
male54
41.2%
2021-05-15T14:28:57.787989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:57.876705image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female77
58.8%
male54
41.2%

Most occurring characters

ValueCountFrequency (%)
e208
30.7%
m131
19.3%
a131
19.3%
l131
19.3%
f77
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter678
100.0%

Most frequent character per category

ValueCountFrequency (%)
e208
30.7%
m131
19.3%
a131
19.3%
l131
19.3%
f77
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Latin678
100.0%

Most frequent character per script

ValueCountFrequency (%)
e208
30.7%
m131
19.3%
a131
19.3%
l131
19.3%
f77
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII678
100.0%

Most frequent character per block

ValueCountFrequency (%)
e208
30.7%
m131
19.3%
a131
19.3%
l131
19.3%
f77
 
11.4%

Splenomegaly
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
79 
female
52 

Length

Max length6
Median length4
Mean length4.79389313
Min length4

Characters and Unicode

Total characters628
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
male79
60.3%
female52
39.7%
2021-05-15T14:28:58.085196image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:58.160983image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male79
60.3%
female52
39.7%

Most occurring characters

ValueCountFrequency (%)
e183
29.1%
m131
20.9%
a131
20.9%
l131
20.9%
f52
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter628
100.0%

Most frequent character per category

ValueCountFrequency (%)
e183
29.1%
m131
20.9%
a131
20.9%
l131
20.9%
f52
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin628
100.0%

Most frequent character per script

ValueCountFrequency (%)
e183
29.1%
m131
20.9%
a131
20.9%
l131
20.9%
f52
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII628
100.0%

Most frequent character per block

ValueCountFrequency (%)
e183
29.1%
m131
20.9%
a131
20.9%
l131
20.9%
f52
 
8.3%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
97 
female
34 

Length

Max length6
Median length4
Mean length4.519083969
Min length4

Characters and Unicode

Total characters592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
male97
74.0%
female34
 
26.0%
2021-05-15T14:28:58.364956image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:58.437800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male97
74.0%
female34
 
26.0%

Most occurring characters

ValueCountFrequency (%)
e165
27.9%
m131
22.1%
a131
22.1%
l131
22.1%
f34
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter592
100.0%

Most frequent character per category

ValueCountFrequency (%)
e165
27.9%
m131
22.1%
a131
22.1%
l131
22.1%
f34
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin592
100.0%

Most frequent character per script

ValueCountFrequency (%)
e165
27.9%
m131
22.1%
a131
22.1%
l131
22.1%
f34
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII592
100.0%

Most frequent character per block

ValueCountFrequency (%)
e165
27.9%
m131
22.1%
a131
22.1%
l131
22.1%
f34
 
5.7%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
100 
male
31 

Length

Max length6
Median length6
Mean length5.526717557
Min length4

Characters and Unicode

Total characters724
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowfemale
ValueCountFrequency (%)
female100
76.3%
male31
 
23.7%
2021-05-15T14:28:58.632284image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:58.717066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female100
76.3%
male31
 
23.7%

Most occurring characters

ValueCountFrequency (%)
e231
31.9%
m131
18.1%
a131
18.1%
l131
18.1%
f100
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter724
100.0%

Most frequent character per category

ValueCountFrequency (%)
e231
31.9%
m131
18.1%
a131
18.1%
l131
18.1%
f100
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin724
100.0%

Most frequent character per script

ValueCountFrequency (%)
e231
31.9%
m131
18.1%
a131
18.1%
l131
18.1%
f100
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII724
100.0%

Most frequent character per block

ValueCountFrequency (%)
e231
31.9%
m131
18.1%
a131
18.1%
l131
18.1%
f100
13.8%

Liver Metastasis
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
female
102 
male
29 

Length

Max length6
Median length6
Mean length5.557251908
Min length4

Characters and Unicode

Total characters728
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale
ValueCountFrequency (%)
female102
77.9%
male29
 
22.1%
2021-05-15T14:28:58.916629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:59.009419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
female102
77.9%
male29
 
22.1%

Most occurring characters

ValueCountFrequency (%)
e233
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f102
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter728
100.0%

Most frequent character per category

ValueCountFrequency (%)
e233
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f102
14.0%

Most occurring scripts

ValueCountFrequency (%)
Latin728
100.0%

Most frequent character per script

ValueCountFrequency (%)
e233
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f102
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII728
100.0%

Most frequent character per block

ValueCountFrequency (%)
e233
32.0%
m131
18.0%
a131
18.0%
l131
18.0%
f102
14.0%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
male
92 
female
39 

Length

Max length6
Median length4
Mean length4.595419847
Min length4

Characters and Unicode

Total characters602
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale
ValueCountFrequency (%)
male92
70.2%
female39
29.8%
2021-05-15T14:28:59.177926image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:28:59.260705image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
male92
70.2%
female39
29.8%

Most occurring characters

ValueCountFrequency (%)
e170
28.2%
m131
21.8%
a131
21.8%
l131
21.8%
f39
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter602
100.0%

Most frequent character per category

ValueCountFrequency (%)
e170
28.2%
m131
21.8%
a131
21.8%
l131
21.8%
f39
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
Latin602
100.0%

Most frequent character per script

ValueCountFrequency (%)
e170
28.2%
m131
21.8%
a131
21.8%
l131
21.8%
f39
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII602
100.0%

Most frequent character per block

ValueCountFrequency (%)
e170
28.2%
m131
21.8%
a131
21.8%
l131
21.8%
f39
 
6.5%

Age at diagnosis
Real number (ℝ≥0)

Distinct46
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.57251908
Minimum20
Maximum93
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:28:59.349469image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile44
Q159
median66
Q374
95-th percentile83.5
Maximum93
Range73
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.59061344
Coefficient of variation (CV)0.1920105192
Kurtosis1.592002703
Mean65.57251908
Median Absolute Deviation (MAD)8
Skewness-0.8595353272
Sum8590
Variance158.5235467
MonotocityNot monotonic
2021-05-15T14:28:59.513082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
737
 
5.3%
626
 
4.6%
746
 
4.6%
636
 
4.6%
615
 
3.8%
765
 
3.8%
725
 
3.8%
715
 
3.8%
665
 
3.8%
645
 
3.8%
Other values (36)76
58.0%
ValueCountFrequency (%)
201
0.8%
231
0.8%
271
0.8%
401
0.8%
412
1.5%
ValueCountFrequency (%)
931
0.8%
881
0.8%
871
0.8%
861
0.8%
851
0.8%

Grams of Alcohol per day
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.44274809
Minimum0
Maximum500
Zeros28
Zeros (%)21.4%
Memory size1.1 KiB
2021-05-15T14:28:59.629668image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155
median79
Q3100
95-th percentile200
Maximum500
Range500
Interquartile range (IQR)45

Descriptive statistics

Standard deviation65.47168035
Coefficient of variation (CV)0.8241366509
Kurtosis12.93664555
Mean79.44274809
Median Absolute Deviation (MAD)21
Skewness2.392068911
Sum10407
Variance4286.540928
MonotocityNot monotonic
2021-05-15T14:28:59.758555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7936
27.5%
10029
22.1%
028
21.4%
2007
 
5.3%
756
 
4.6%
805
 
3.8%
703
 
2.3%
503
 
2.3%
602
 
1.5%
1202
 
1.5%
Other values (9)10
 
7.6%
ValueCountFrequency (%)
028
21.4%
201
 
0.8%
401
 
0.8%
503
 
2.3%
602
 
1.5%
ValueCountFrequency (%)
5001
 
0.8%
3001
 
0.8%
2007
5.3%
1802
 
1.5%
1501
 
0.8%

Packs of cigarets per year:
Real number (ℝ≥0)

ZEROS

Distinct26
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.09923664
Minimum0
Maximum510
Zeros46
Zeros (%)35.1%
Memory size1.1 KiB
2021-05-15T14:28:59.864131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q323
95-th percentile60
Maximum510
Range510
Interquartile range (IQR)23

Descriptive statistics

Standard deviation46.92930116
Coefficient of variation (CV)2.031638616
Kurtosis90.35860348
Mean23.09923664
Median Absolute Deviation (MAD)21
Skewness8.744800475
Sum3026
Variance2202.359307
MonotocityNot monotonic
2021-05-15T14:29:00.006875image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
046
35.1%
2344
33.6%
307
 
5.3%
605
 
3.8%
505
 
3.8%
102
 
1.5%
402
 
1.5%
152
 
1.5%
67.51
 
0.8%
34.51
 
0.8%
Other values (16)16
 
12.2%
ValueCountFrequency (%)
046
35.1%
11
 
0.8%
21
 
0.8%
7.51
 
0.8%
81
 
0.8%
ValueCountFrequency (%)
5101
 
0.8%
801
 
0.8%
781
 
0.8%
67.51
 
0.8%
605
3.8%
Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
61 
1
26 
2
25 
3
14 
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters131
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0
ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%
2021-05-15T14:29:00.235822image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:29:00.303683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%

Most occurring characters

ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number131
100.0%

Most frequent character per category

ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common131
100.0%

Most frequent character per script

ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII131
100.0%

Most frequent character per block

ValueCountFrequency (%)
061
46.6%
126
19.8%
225
19.1%
314
 
10.7%
45
 
3.8%
Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
113 
2
15 
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters131
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%
2021-05-15T14:29:00.509091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:29:00.574915image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number131
100.0%

Most frequent character per category

ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common131
100.0%

Most frequent character per script

ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII131
100.0%

Most frequent character per block

ValueCountFrequency (%)
1113
86.3%
215
 
11.5%
33
 
2.3%

Ascites degree
Categorical

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
83 
2
34 
3
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters131
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1
ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%
2021-05-15T14:29:00.781096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:29:00.844925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%

Most occurring characters

ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number131
100.0%

Most frequent character per category

ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common131
100.0%

Most frequent character per script

ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII131
100.0%

Most frequent character per block

ValueCountFrequency (%)
183
63.4%
234
26.0%
314
 
10.7%

International Normalised Ratio:
Real number (ℝ≥0)

Distinct78
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.436870229
Minimum0.94
Maximum4.82
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:00.946766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.94
5-th percentile1.01
Q11.18
median1.32
Q31.53
95-th percentile2.075
Maximum4.82
Range3.88
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.4999832058
Coefficient of variation (CV)0.3479668489
Kurtosis19.01542955
Mean1.436870229
Median Absolute Deviation (MAD)0.15
Skewness3.706491875
Sum188.23
Variance0.2499832061
MonotocityNot monotonic
2021-05-15T14:29:01.107463image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.175
 
3.8%
1.25
 
3.8%
1.394
 
3.1%
1.334
 
3.1%
1.324
 
3.1%
1.113
 
2.3%
1.463
 
2.3%
1.183
 
2.3%
1.533
 
2.3%
1.233
 
2.3%
Other values (68)94
71.8%
ValueCountFrequency (%)
0.942
1.5%
0.951
0.8%
0.962
1.5%
0.971
0.8%
1.012
1.5%
ValueCountFrequency (%)
4.821
0.8%
3.561
0.8%
3.161
0.8%
3.141
0.8%
2.421
0.8%

Alpha-Fetoprotein (ng/mL)
Real number (ℝ≥0)

Distinct108
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22426.55824
Minimum1.2
Maximum1810346
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:01.270581image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile2.2
Q15.2
median42
Q3696.5
95-th percentile30888
Maximum1810346
Range1810344.8
Interquartile range (IQR)691.3

Descriptive statistics

Standard deviation162775.711
Coefficient of variation (CV)7.25816727
Kurtosis114.4854863
Mean22426.55824
Median Absolute Deviation (MAD)39.5
Skewness10.47341316
Sum2937879.13
Variance2.64959321 × 1010
MonotocityNot monotonic
2021-05-15T14:29:01.410138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224277
 
5.3%
3.13
 
2.3%
2.53
 
2.3%
203
 
2.3%
8.82
 
1.5%
52
 
1.5%
772
 
1.5%
2.92
 
1.5%
2.82
 
1.5%
2.62
 
1.5%
Other values (98)103
78.6%
ValueCountFrequency (%)
1.21
0.8%
1.71
0.8%
1.81
0.8%
1.91
0.8%
22
1.5%
ValueCountFrequency (%)
18103461
0.8%
4215001
0.8%
1852031
0.8%
1008091
0.8%
506551
0.8%

Haemoglobin (g/dL)
Real number (ℝ≥0)

Distinct59
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.88839695
Minimum5
Maximum18.7
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:01.644468image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9.5
Q111.55
median13
Q314.5
95-th percentile15.8
Maximum18.7
Range13.7
Interquartile range (IQR)2.95

Descriptive statistics

Standard deviation2.126891128
Coefficient of variation (CV)0.1650237137
Kurtosis0.680457805
Mean12.88839695
Median Absolute Deviation (MAD)1.5
Skewness-0.4266363189
Sum1688.38
Variance4.523665872
MonotocityNot monotonic
2021-05-15T14:29:02.410044image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.68
 
6.1%
14.96
 
4.6%
13.15
 
3.8%
135
 
3.8%
12.14
 
3.1%
10.84
 
3.1%
13.74
 
3.1%
14.34
 
3.1%
14.63
 
2.3%
15.73
 
2.3%
Other values (49)85
64.9%
ValueCountFrequency (%)
51
 
0.8%
7.31
 
0.8%
8.91
 
0.8%
9.12
1.5%
9.53
2.3%
ValueCountFrequency (%)
18.71
0.8%
16.61
0.8%
16.42
1.5%
16.21
0.8%
15.91
0.8%

Mean Corpuscular Volume (fl)
Real number (ℝ≥0)

Distinct108
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.50229008
Minimum69.5
Maximum119
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:02.560685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum69.5
5-th percentile82
Q189.6
median94.7
Q399.85
95-th percentile105.75
Maximum119
Range49.5
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation8.010228707
Coefficient of variation (CV)0.08476227085
Kurtosis0.8588469503
Mean94.50229008
Median Absolute Deviation (MAD)5.1
Skewness-0.2649357565
Sum12379.8
Variance64.16376395
MonotocityNot monotonic
2021-05-15T14:29:02.702304image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.15
 
3.8%
96.13
 
2.3%
93.83
 
2.3%
89.53
 
2.3%
103.62
 
1.5%
96.72
 
1.5%
100.82
 
1.5%
1022
 
1.5%
90.12
 
1.5%
90.92
 
1.5%
Other values (98)105
80.2%
ValueCountFrequency (%)
69.51
0.8%
70.61
0.8%
741
0.8%
78.71
0.8%
79.81
0.8%
ValueCountFrequency (%)
1191
0.8%
111.41
0.8%
1111
0.8%
109.31
0.8%
107.51
0.8%

Leukocytes(G/L)
Real number (ℝ≥0)

Distinct88
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.343137
Minimum2.2
Maximum13000
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:02.868378image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.6
Q15
median6.86
Q31608.34
95-th percentile8700
Maximum13000
Range12997.8
Interquartile range (IQR)1603.34

Descriptive statistics

Standard deviation3044.251233
Coefficient of variation (CV)1.892787156
Kurtosis2.097582026
Mean1608.343137
Median Absolute Deviation (MAD)2.56
Skewness1.785868834
Sum210692.951
Variance9267465.567
MonotocityNot monotonic
2021-05-15T14:29:03.037201image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.96
 
4.6%
5.55
 
3.8%
4.34
 
3.1%
6.14
 
3.1%
5.83
 
2.3%
5.23
 
2.3%
53
 
2.3%
5.43
 
2.3%
9.83
 
2.3%
93
 
2.3%
Other values (78)94
71.8%
ValueCountFrequency (%)
2.21
0.8%
2.31
0.8%
2.421
0.8%
2.91
0.8%
31
0.8%
ValueCountFrequency (%)
130001
0.8%
104001
0.8%
99001
0.8%
96001
0.8%
95001
0.8%

Platelets (G/L)
Real number (ℝ≥0)

Distinct110
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105103.8604
Minimum1.71
Maximum433000
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:03.176826image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.71
5-th percentile69
Q1205.5
median91000
Q3163500
95-th percentile285000
Maximum433000
Range432998.29
Interquartile range (IQR)163294.5

Descriptive statistics

Standard deviation100830.0818
Coefficient of variation (CV)0.9593375682
Kurtosis0.3148066803
Mean105103.8604
Median Absolute Deviation (MAD)90444
Skewness0.8903907301
Sum13768605.71
Variance1.01667054 × 1010
MonotocityNot monotonic
2021-05-15T14:29:03.334449image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
770005
 
3.8%
1090003
 
2.3%
993
 
2.3%
910003
 
2.3%
1082
 
1.5%
1590002
 
1.5%
1051042
 
1.5%
1940002
 
1.5%
582
 
1.5%
2750002
 
1.5%
Other values (100)105
80.2%
ValueCountFrequency (%)
1.711
0.8%
511
0.8%
582
1.5%
601
0.8%
611
0.8%
ValueCountFrequency (%)
4330001
0.8%
4060001
0.8%
3510001
0.8%
3180001
0.8%
3090001
0.8%

Albumin (mg/dL)
Real number (ℝ≥0)

Distinct37
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.410076336
Minimum1.9
Maximum4.9
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:03.475029image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile2.3
Q13
median3.4
Q34
95-th percentile4.5
Maximum4.9
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.683057038
Coefficient of variation (CV)0.2003054978
Kurtosis-0.6769021478
Mean3.410076336
Median Absolute Deviation (MAD)0.5
Skewness-0.08898134575
Sum446.72
Variance0.4665669172
MonotocityNot monotonic
2021-05-15T14:29:03.608473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
4.212
 
9.2%
3.111
 
8.4%
3.28
 
6.1%
4.17
 
5.3%
3.46
 
4.6%
2.76
 
4.6%
3.56
 
4.6%
2.46
 
4.6%
3.86
 
4.6%
3.65
 
3.8%
Other values (27)58
44.3%
ValueCountFrequency (%)
1.92
1.5%
2.12
1.5%
2.22
1.5%
2.32
1.5%
2.351
0.8%
ValueCountFrequency (%)
4.91
 
0.8%
4.72
1.5%
4.61
 
0.8%
4.541
 
0.8%
4.53
2.3%

Total Bilirubin(mg/dL)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.242366412
Minimum0.3
Maximum40.5
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:03.792979image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.9
median1.5
Q33.24
95-th percentile9.7
Maximum40.5
Range40.2
Interquartile range (IQR)2.34

Descriptive statistics

Standard deviation5.5495922
Coefficient of variation (CV)1.711587
Kurtosis23.88492655
Mean3.242366412
Median Absolute Deviation (MAD)0.8
Skewness4.566397334
Sum424.75
Variance30.79797359
MonotocityNot monotonic
2021-05-15T14:29:03.934602image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.58
 
6.1%
18
 
6.1%
0.87
 
5.3%
1.37
 
5.3%
1.46
 
4.6%
0.76
 
4.6%
0.96
 
4.6%
1.75
 
3.8%
0.64
 
3.1%
2.14
 
3.1%
Other values (47)70
53.4%
ValueCountFrequency (%)
0.31
 
0.8%
0.43
 
2.3%
0.58
6.1%
0.64
3.1%
0.76
4.6%
ValueCountFrequency (%)
40.51
0.8%
32.31
0.8%
28.91
0.8%
191
0.8%
161
0.8%

Alanine transaminase (U/L)
Real number (ℝ≥0)

Distinct83
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.21709924
Minimum11
Maximum420
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:04.091913image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile18.5
Q129.5
median50
Q392
95-th percentile163
Maximum420
Range409
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation60.33852721
Coefficient of variation (CV)0.8717286317
Kurtosis9.674968238
Mean69.21709924
Median Absolute Deviation (MAD)23
Skewness2.5501831
Sum9067.44
Variance3640.737865
MonotocityNot monotonic
2021-05-15T14:29:04.257008image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
355
 
3.8%
285
 
3.8%
314
 
3.1%
434
 
3.1%
274
 
3.1%
254
 
3.1%
344
 
3.1%
264
 
3.1%
563
 
2.3%
423
 
2.3%
Other values (73)91
69.5%
ValueCountFrequency (%)
112
1.5%
131
0.8%
162
1.5%
171
0.8%
181
0.8%
ValueCountFrequency (%)
4201
0.8%
2991
0.8%
2621
0.8%
2171
0.8%
2041
0.8%

Aspartate transaminase (U/L)
Real number (ℝ≥0)

Distinct98
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9236641
Minimum17
Maximum553
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:04.396513image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile27.5
Q145.5
median74
Q3113.5
95-th percentile315.5
Maximum553
Range536
Interquartile range (IQR)68

Descriptive statistics

Standard deviation91.21391749
Coefficient of variation (CV)0.90379118
Kurtosis7.996851338
Mean100.9236641
Median Absolute Deviation (MAD)33
Skewness2.56357421
Sum13221
Variance8319.978743
MonotocityNot monotonic
2021-05-15T14:29:04.562067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
855
 
3.8%
384
 
3.1%
743
 
2.3%
293
 
2.3%
1013
 
2.3%
433
 
2.3%
863
 
2.3%
262
 
1.5%
412
 
1.5%
872
 
1.5%
Other values (88)101
77.1%
ValueCountFrequency (%)
172
1.5%
191
0.8%
231
0.8%
262
1.5%
271
0.8%
ValueCountFrequency (%)
5531
0.8%
5231
0.8%
3571
0.8%
3541
0.8%
3351
0.8%
Distinct117
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.0938931
Minimum23
Maximum1575
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:04.702248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile39
Q190.5
median184
Q3320.5
95-th percentile824.5
Maximum1575
Range1552
Interquartile range (IQR)230

Descriptive statistics

Standard deviation262.2275514
Coefficient of variation (CV)0.9929330373
Kurtosis6.802416814
Mean264.0938931
Median Absolute Deviation (MAD)104
Skewness2.313970387
Sum34596.3
Variance68763.28873
MonotocityNot monotonic
2021-05-15T14:29:04.862233image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233
 
2.3%
1963
 
2.3%
1153
 
2.3%
2642
 
1.5%
3392
 
1.5%
722
 
1.5%
1842
 
1.5%
1432
 
1.5%
3112
 
1.5%
542
 
1.5%
Other values (107)108
82.4%
ValueCountFrequency (%)
233
2.3%
331
 
0.8%
341
 
0.8%
351
 
0.8%
381
 
0.8%
ValueCountFrequency (%)
15751
0.8%
13901
0.8%
9931
0.8%
9241
0.8%
8791
0.8%

Alkaline phosphatase (U/L)
Real number (ℝ≥0)

Distinct104
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.1090076
Minimum1.28
Maximum980
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:05.030344image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.28
5-th percentile68
Q1109.5
median165
Q3241.5
95-th percentile578.5
Maximum980
Range978.72
Interquartile range (IQR)132

Descriptive statistics

Standard deviation172.9486959
Coefficient of variation (CV)0.8040048985
Kurtosis7.033855629
Mean215.1090076
Median Absolute Deviation (MAD)61
Skewness2.447419133
Sum28179.28
Variance29911.2514
MonotocityNot monotonic
2021-05-15T14:29:05.170968image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1094
 
3.1%
973
 
2.3%
1203
 
2.3%
1133
 
2.3%
1743
 
2.3%
1582
 
1.5%
1662
 
1.5%
1462
 
1.5%
682
 
1.5%
922
 
1.5%
Other values (94)105
80.2%
ValueCountFrequency (%)
1.281
0.8%
551
0.8%
561
0.8%
621
0.8%
631
0.8%
ValueCountFrequency (%)
9801
0.8%
9741
0.8%
9231
0.8%
6841
0.8%
6701
0.8%

Total Proteins (g/dL)
Real number (ℝ≥0)

Distinct45
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.690839695
Minimum3.9
Maximum78
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:05.322514image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile5.3
Q16.3
median7.1
Q37.75
95-th percentile8.75
Maximum78
Range74.1
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation9.707558121
Coefficient of variation (CV)1.116987364
Kurtosis35.81550686
Mean8.690839695
Median Absolute Deviation (MAD)0.8
Skewness5.926255924
Sum1138.5
Variance94.23668467
MonotocityNot monotonic
2021-05-15T14:29:05.478102image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
7.310
 
7.6%
8.79
 
6.9%
6.38
 
6.1%
76
 
4.6%
7.16
 
4.6%
6.76
 
4.6%
5.45
 
3.8%
6.95
 
3.8%
7.75
 
3.8%
7.25
 
3.8%
Other values (35)66
50.4%
ValueCountFrequency (%)
3.91
 
0.8%
4.31
 
0.8%
4.91
 
0.8%
53
2.3%
5.21
 
0.8%
ValueCountFrequency (%)
781
0.8%
691
0.8%
581
0.8%
371
0.8%
16.81
0.8%

Creatinine (mg/dL)
Real number (ℝ≥0)

Distinct77
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194503817
Minimum0.2
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:05.631686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.54
Q10.705
median0.86
Q31.2
95-th percentile2.95
Maximum7.6
Range7.4
Interquartile range (IQR)0.495

Descriptive statistics

Standard deviation0.9796052688
Coefficient of variation (CV)0.8200938792
Kurtosis15.90111755
Mean1.194503817
Median Absolute Deviation (MAD)0.18
Skewness3.447301924
Sum156.48
Variance0.9596264827
MonotocityNot monotonic
2021-05-15T14:29:05.782283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.78
 
6.1%
0.87
 
5.3%
0.97
 
5.3%
2.956
 
4.6%
0.774
 
3.1%
0.793
 
2.3%
0.863
 
2.3%
0.833
 
2.3%
0.883
 
2.3%
0.713
 
2.3%
Other values (67)84
64.1%
ValueCountFrequency (%)
0.21
0.8%
0.381
0.8%
0.41
0.8%
0.482
1.5%
0.521
0.8%
ValueCountFrequency (%)
7.61
0.8%
4.951
0.8%
4.821
0.8%
3.231
0.8%
3.131
0.8%

Number of Nodules
Real number (ℝ≥0)

Distinct6
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.717557252
Minimum0
Maximum5
Zeros1
Zeros (%)0.8%
Memory size1.1 KiB
2021-05-15T14:29:05.904466image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.794327854
Coefficient of variation (CV)0.6602723284
Kurtosis-1.690718497
Mean2.717557252
Median Absolute Deviation (MAD)1
Skewness0.3442644434
Sum356
Variance3.219612449
MonotocityNot monotonic
2021-05-15T14:29:06.015979image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
153
40.5%
546
35.1%
222
16.8%
37
 
5.3%
42
 
1.5%
01
 
0.8%
ValueCountFrequency (%)
01
 
0.8%
153
40.5%
222
16.8%
37
 
5.3%
42
 
1.5%
ValueCountFrequency (%)
546
35.1%
42
 
1.5%
37
 
5.3%
222
16.8%
153
40.5%

Major dimension of nodule (cm)
Real number (ℝ≥0)

Distinct63
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.855725191
Minimum1.5
Maximum22
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:06.128862image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2
Q13.25
median6
Q38.3
95-th percentile17.25
Maximum22
Range20.5
Interquartile range (IQR)5.05

Descriptive statistics

Standard deviation4.730131081
Coefficient of variation (CV)0.6899534257
Kurtosis1.40015111
Mean6.855725191
Median Absolute Deviation (MAD)2.5
Skewness1.370053388
Sum898.1
Variance22.37414005
MonotocityNot monotonic
2021-05-15T14:29:06.278507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8518
 
13.7%
27
 
5.3%
36
 
4.6%
45
 
3.8%
105
 
3.8%
3.54
 
3.1%
2.34
 
3.1%
204
 
3.1%
4.73
 
2.3%
93
 
2.3%
Other values (53)72
55.0%
ValueCountFrequency (%)
1.53
2.3%
1.81
 
0.8%
1.91
 
0.8%
27
5.3%
2.11
 
0.8%
ValueCountFrequency (%)
221
 
0.8%
204
3.1%
191
 
0.8%
17.51
 
0.8%
171
 
0.8%

Direct Bilirubin (mg/dL)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct38
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.96259542
Minimum0.1
Maximum29.3
Zeros0
Zeros (%)0.0%
Memory size1.1 KiB
2021-05-15T14:29:06.405713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.5
median1.2
Q31.96
95-th percentile5.05
Maximum29.3
Range29.2
Interquartile range (IQR)1.46

Descriptive statistics

Standard deviation3.68818938
Coefficient of variation (CV)1.879240797
Kurtosis32.85943989
Mean1.96259542
Median Absolute Deviation (MAD)0.76
Skewness5.423718206
Sum257.1
Variance13.6027409
MonotocityNot monotonic
2021-05-15T14:29:06.570328image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1.9632
24.4%
0.316
12.2%
0.510
 
7.6%
0.78
 
6.1%
0.27
 
5.3%
15
 
3.8%
1.14
 
3.1%
0.84
 
3.1%
1.93
 
2.3%
1.43
 
2.3%
Other values (28)39
29.8%
ValueCountFrequency (%)
0.11
 
0.8%
0.27
5.3%
0.316
12.2%
0.371
 
0.8%
0.43
 
2.3%
ValueCountFrequency (%)
29.31
0.8%
22.11
0.8%
19.51
0.8%
9.71
0.8%
9.61
0.8%

Iron (mcg/dL)
Real number (ℝ≥0)

Distinct63
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.83587786
Minimum0
Maximum224
Zeros1
Zeros (%)0.8%
Memory size1.1 KiB
2021-05-15T14:29:06.757766image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.5
Q160
median86
Q390
95-th percentile182.5
Maximum224
Range224
Interquartile range (IQR)30

Descriptive statistics

Standard deviation43.15494814
Coefficient of variation (CV)0.5027611905
Kurtosis1.147362734
Mean85.83587786
Median Absolute Deviation (MAD)8
Skewness0.8209946187
Sum11244.5
Variance1862.349549
MonotocityNot monotonic
2021-05-15T14:29:06.890436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8654
41.2%
943
 
2.3%
933
 
2.3%
1843
 
2.3%
372
 
1.5%
152
 
1.5%
1442
 
1.5%
262
 
1.5%
872
 
1.5%
282
 
1.5%
Other values (53)56
42.7%
ValueCountFrequency (%)
01
0.8%
91
0.8%
131
0.8%
152
1.5%
191
0.8%
ValueCountFrequency (%)
2241
 
0.8%
2001
 
0.8%
1971
 
0.8%
1871
 
0.8%
1843
2.3%

Oxygen Saturation (%)
Real number (ℝ≥0)

Distinct50
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.95007634
Minimum0
Maximum126
Zeros1
Zeros (%)0.8%
Memory size1.1 KiB
2021-05-15T14:29:07.039441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.5
Q125
median37
Q337
95-th percentile88
Maximum126
Range126
Interquartile range (IQR)12

Descriptive statistics

Standard deviation22.10383432
Coefficient of variation (CV)0.5982080826
Kurtosis2.523754043
Mean36.95007634
Median Absolute Deviation (MAD)6
Skewness1.282012787
Sum4840.46
Variance488.5794915
MonotocityNot monotonic
2021-05-15T14:29:07.262708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3759
45.0%
254
 
3.1%
274
 
3.1%
563
 
2.3%
173
 
2.3%
32
 
1.5%
732
 
1.5%
332
 
1.5%
342
 
1.5%
242
 
1.5%
Other values (40)48
36.6%
ValueCountFrequency (%)
01
0.8%
1.521
0.8%
2.261
0.8%
32
1.5%
62
1.5%
ValueCountFrequency (%)
1261
0.8%
991
0.8%
961
0.8%
952
1.5%
901
0.8%

Ferritin
Real number (ℝ≥0)

Distinct75
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.8152672
Minimum0
Maximum2230
Zeros1
Zeros (%)0.8%
Memory size1.1 KiB
2021-05-15T14:29:07.417870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.5
Q1266.5
median435
Q3435
95-th percentile932.5
Maximum2230
Range2230
Interquartile range (IQR)168.5

Descriptive statistics

Standard deviation340.6914953
Coefficient of variation (CV)0.7835315847
Kurtosis10.16998618
Mean434.8152672
Median Absolute Deviation (MAD)80
Skewness2.466325685
Sum56960.8
Variance116070.695
MonotocityNot monotonic
2021-05-15T14:29:07.565476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43556
42.7%
482
 
1.5%
01
 
0.8%
1411
 
0.8%
21651
 
0.8%
221
 
0.8%
3021
 
0.8%
10011
 
0.8%
76.91
 
0.8%
2211
 
0.8%
Other values (65)65
49.6%
ValueCountFrequency (%)
01
0.8%
161
0.8%
181
0.8%
201
0.8%
221
0.8%
ValueCountFrequency (%)
22301
0.8%
21651
0.8%
14521
0.8%
13161
0.8%
10011
0.8%

Class
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
80 
0
51 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters131
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
180
61.1%
051
38.9%
2021-05-15T14:29:07.894595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-05-15T14:29:07.960462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
180
61.1%
051
38.9%

Most occurring characters

ValueCountFrequency (%)
180
61.1%
051
38.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number131
100.0%

Most frequent character per category

ValueCountFrequency (%)
180
61.1%
051
38.9%

Most occurring scripts

ValueCountFrequency (%)
Common131
100.0%

Most frequent character per script

ValueCountFrequency (%)
180
61.1%
051
38.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII131
100.0%

Most frequent character per block

ValueCountFrequency (%)
180
61.1%
051
38.9%

Interactions

2021-05-15T14:27:47.238501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.356223image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.462388image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.577043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.691123image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.795823image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:47.905914image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.013111image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.117794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.224548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.338204image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.452948image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.559652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.761112image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.869841image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:48.977892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.087048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.213747image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.318775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.425164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.524897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.631639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.732642image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.835583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:49.934693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:50.042517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:50.149796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:50.252012image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:27:50.351790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
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2021-05-15T14:28:40.128615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.266203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.390436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.519137image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.625808image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.748518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:40.865168image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.001800image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.114499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.233181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.339897image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.463850image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.566168image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.671984image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.778746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:41.886087image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.004819image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.097612image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.209801image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.304066image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.393870image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.506523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.606256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.735952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.835643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:42.955322image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.059086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.214683image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.320346image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.435081image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.532821image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.633615image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.750305image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.847096image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:43.980289image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.083035image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.205746image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.321441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.458405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.562676image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.687854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.811563image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:44.912293image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:45.041907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:45.155657image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:45.325148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.110051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.242738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.343518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.549920image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.708290image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.804585image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:46.900816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.062895image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.218439image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.314225image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.477754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.595430image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.729073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:47.884202image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.066712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.164452image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.298792image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.454360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.572023image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.666805image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.790476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.893160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:48.998876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.096614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.205330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.306058image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.475613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.578371image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.663141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.792756image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-05-15T14:28:49.913518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-05-15T14:29:08.092068image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-15T14:29:08.585151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-15T14:29:09.038754image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-15T14:29:09.522501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-15T14:29:10.099721image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-15T14:28:50.400238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-15T14:28:52.341575image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

GenderSymptomsAlcoholHepatitis B Surface AntigenHepatitis B e AntigenHepatitis B Core AntibodyHepatitis C Virus AntibodyCirrhosisEndemic CountriesSmokingDiabetesObesityHemochromatosisArterial HypertensionChronic Renal InsufficiencyHuman Immunodeficiency VirusNonalcoholic SteatohepatitisEsophageal VaricesSplenomegalyPortal HypertensionPortal Vein ThrombosisLiver MetastasisRadiological HallmarkAge at diagnosisGrams of Alcohol per dayPacks of cigarets per year:Performance StatusEncefalopathy degreeAscites degreeInternational Normalised Ratio:Alpha-Fetoprotein (ng/mL)Haemoglobin (g/dL)Mean Corpuscular Volume (fl)Leukocytes(G/L)Platelets (G/L)Albumin (mg/dL)Total Bilirubin(mg/dL)Alanine transaminase (U/L)Aspartate transaminase (U/L)Gamma glutamyl transferase (U/L)Alkaline phosphatase (U/L)Total Proteins (g/dL)Creatinine (mg/dL)Number of NodulesMajor dimension of nodule (cm)Direct Bilirubin (mg/dL)Iron (mcg/dL)Oxygen Saturation (%)FerritinClass
0malefemalemalefemalefemalefemalefemalemalefemalemalemalefemalemalefemalefemalefemalefemalemalefemalefemalefemalefemalemale6713715.00111.5395.013.70106.64.9099.03.402.1034.0041183.0150.07.10.7013.50.5086.037.0435.01
1femalemalefemalefemalefemalefemalemalemalefemalemalemalefemalefemalemalefemalefemalefemalemalefemalefemalefemalefemalemale62023.00111.3922427.012.7994.51608.34105104.03.413.2469.22101264.0215.08.72.9511.81.9686.037.0435.01
2malefemalemalemalefemalemalefemalemalefemalemalefemalefemalefemalemalemalefemalefemalefemalefemalemalefemalemalemale785050.02120.965.88.9079.88.40472.03.300.4058.0068202.0109.07.02.10513.00.1028.06.016.01
3malemalemalefemalefemalefemalefemalemalefemalemalemalefemalefemalemalefemalefemalefemalefemalefemalefemalefemalemalemale774030.00110.952440.013.4097.19.00279.03.700.4016.006494.0174.08.11.11215.70.2086.037.0435.00
4malemalemalemalefemalemalefemalemalefemalemalefemalefemalefemalemalemalefemalefemalefemalefemalefemalefemalefemalemale7610030.00110.9449.014.3095.16.40199.04.100.70147.00306173.0109.06.91.8019.01.9659.015.022.01
5malefemalemalefemalefemalefemalefemalemalefemalemalefemalemalefemalefemalefemalefemalefemalemalemalemalefemalefemalemale757923.01121.58110.013.4091.55.4085.03.403.5091.00122242.0396.05.60.90110.01.4053.022.0111.00
6malefemalefemalefemalefemalemalemalemalefemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalemale4900.00111.40138.910.40102.03.2042000.02.352.72119.00183143.0211.07.30.8052.62.19171.0126.01452.00
7malemalemalefemalefemalefemalefemalemalefemalemalemalefemalefemalefemalefemalefemalefemalefemalemalemalemalefemalemale617920.03111.469860.010.8092.03.0058.03.103.2079.00108184.0300.07.10.5229.01.3042.025.0706.00
8malemalemalefemalefemalefemalefemalemalefemalemalemalefemalefemalemalefemalefemalefemalefemalemalemalefemalefemalemale5010032.01123.148.811.90107.54.9070.01.903.3026.0059115.063.06.10.5916.41.2085.073.0982.01
9malemalemalefemalefemalefemalefemalemalefemalefemalefemalefemalemalefemalefemalefemalefemalefemalemalefemalefemalefemalefemale431000.00111.121.811.8087.85100.00193000.04.200.5071.0045256.0303.07.10.5919.30.7086.037.0435.01

Last rows

GenderSymptomsAlcoholHepatitis B Surface AntigenHepatitis B e AntigenHepatitis B Core AntibodyHepatitis C Virus AntibodyCirrhosisEndemic CountriesSmokingDiabetesObesityHemochromatosisArterial HypertensionChronic Renal InsufficiencyHuman Immunodeficiency VirusNonalcoholic SteatohepatitisEsophageal VaricesSplenomegalyPortal HypertensionPortal Vein ThrombosisLiver MetastasisRadiological HallmarkAge at diagnosisGrams of Alcohol per dayPacks of cigarets per year:Performance StatusEncefalopathy degreeAscites degreeInternational Normalised Ratio:Alpha-Fetoprotein (ng/mL)Haemoglobin (g/dL)Mean Corpuscular Volume (fl)Leukocytes(G/L)Platelets (G/L)Albumin (mg/dL)Total Bilirubin(mg/dL)Alanine transaminase (U/L)Aspartate transaminase (U/L)Gamma glutamyl transferase (U/L)Alkaline phosphatase (U/L)Total Proteins (g/dL)Creatinine (mg/dL)Number of NodulesMajor dimension of nodule (cm)Direct Bilirubin (mg/dL)Iron (mcg/dL)Oxygen Saturation (%)FerritinClass
121malefemalemalefemalefemalefemalefemalemalefemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalemale591000.00111.943.110.8102.85300.097000.03.601.1119.0125663.0433.06.50.8723.20.4052.537.0856.00
122malemalemalefemalefemalemalemalemalefemalemalefemalefemalefemalefemalefemalefemalefemalemalemalemalemalemalefemale517552.52111.5650655.09.885.63900.0132000.02.602.6123.0219503.0363.07.30.5514.01.5040.012.057.00
123malefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalemale7400.00111.111.215.183.85.2178000.04.700.535.01745.3151.06.41.5018.31.9688.027.090.01
124malemalemalefemalefemalemalemalemalefemalemalefemalefemalemalefemalefemalefemalefemalemalefemalemalefemalefemalefemale627923.02121.08657.011.889.29400.0211000.03.200.843.0101646.0466.07.30.7018.31.9686.037.0579.00
125malemalemalefemalefemalefemalefemalemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalefemalemalemalefemalemale7610023.04221.20421500.014.389.59.8309000.03.101.520.044291.0217.06.30.70120.00.5052.017.0832.00
126malefemalefemalefemalefemalefemalemalemalefemalemalemalefemalefemalefemalefemalefemalefemalemalemalemalefemalefemalemale73023.00111.20472.015.688.45.583000.04.002.4117.0128249.0117.07.20.69113.00.7086.037.0435.01
127malefemalemalefemalefemalefemalefemalemalefemalefemalemalefemalefemalemalefemalefemalefemalefemalemalemalemalefemalemale76790.03211.6677.012.3104.22900.060000.03.202.854.038311.0182.06.20.7724.31.0093.047.0307.01
128malefemalefemalemalefemalefemalefemalemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalemalefemalefemalefemalefemale720510.02111.132.112.695.18.7254000.03.680.726.038161.0127.06.91.1124.31.9628.010.0308.00
129femalemalemalefemalefemalefemalefemalemalefemalefemalemalefemalefemalefemalefemalefemalefemalefemalefemalemalefemalefemalemale77750.02221.072.011.683.59.0318000.03.890.923.048319.0171.07.10.6625.81.9686.037.0435.00
130femalemalefemalefemalefemalefemalefemalemalefemalefemalefemalemalefemalemalefemalefemalefemalefemalefemalefemalemalemalefemale7500.02131.224.214.987.415.4179000.03.500.631.061196.0150.05.40.70517.50.3086.037.0435.01